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| data science in neuroscience: Neural Data Science Erik Lee Nylen, Pascal Wallisch, 2017-02-24 A Primer with MATLAB® and PythonTM present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience. This book addresses the snake in the room by providing a beginner's introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility. - Includes discussions of both MATLAB and Python in parallel - Introduces the canonical data analysis cascade, standardizing the data analysis flow - Presents tactics that strategically, tactically, and algorithmically help improve the organization of code |
| data science in neuroscience: Data-Driven Computational Neuroscience Concha Bielza, Pedro Larrañaga, 2020-11-26 Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data. |
| data science in neuroscience: Guide to Research Techniques in Neuroscience Matt Carter, Rachel Essner, Nitsan Goldstein, Manasi Iyer, 2022-03-26 Modern neuroscience research is inherently multidisciplinary, with a wide variety of cutting edge new techniques to explore multiple levels of investigation. This Third Edition of Guide to Research Techniques in Neuroscience provides a comprehensive overview of classical and cutting edge methods including their utility, limitations, and how data are presented in the literature. This book can be used as an introduction to neuroscience techniques for anyone new to the field or as a reference for any neuroscientist while reading papers or attending talks. - Nearly 200 updated full-color illustrations to clearly convey the theory and practice of neuroscience methods - Expands on techniques from previous editions and covers many new techniques including in vivo calcium imaging, fiber photometry, RNA-Seq, brain spheroids, CRISPR-Cas9 genome editing, and more - Clear, straightforward explanations of each technique for anyone new to the field - A broad scope of methods, from noninvasive brain imaging in human subjects, to electrophysiology in animal models, to recombinant DNA technology in test tubes, to transfection of neurons in cell culture - Detailed recommendations on where to find protocols and other resources for specific techniques - Walk-through boxes that guide readers through experiments step-by-step |
| data science in neuroscience: MATLAB for Neuroscientists Pascal Wallisch, Michael E. Lusignan, Marc D. Benayoun, Tanya I. Baker, Adam Seth Dickey, Nicholas G. Hatsopoulos, 2014-01-09 MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional material on the creation of visual stimuli, advanced psychophysics, analysis of LFP data, choice probabilities, synchrony, and advanced spectral analysis. Users at a variety of levels—advanced undergraduates, beginning graduate students, and researchers looking to modernize their skills—will learn to design and implement their own analytical tools, and gain the fluency required to meet the computational needs of neuroscience practitioners. - The first complete volume on MATLAB focusing on neuroscience and psychology applications - Problem-based approach with many examples from neuroscience and cognitive psychology using real data - Illustrated in full color throughout - Careful tutorial approach, by authors who are award-winning educators with strong teaching experience |
| data science in neuroscience: Time Series Modeling of Neuroscience Data Tohru Ozaki, 2012-01-26 Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required.Time Series Modeling of Neuroscience Data shows how to |
| data science in neuroscience: Case Studies in Neural Data Analysis Mark A. Kramer, Uri T. Eden, 2016-11-04 A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. A version of this textbook with all of the examples in Python is available on the MIT Press website. |
| data science in neuroscience: 50 Years of Artificial Intelligence Max Lungarella, 2007-12-10 This Festschrift volume, published in celebration of the 50th Anniversary of Artificial Intelligence, includes 34 refereed papers written by leading researchers in the field of Artificial Intelligence. The papers were carefully selected from the invited lectures given at the 50th Anniversary Summit of AI, held at the Centro Stefano Franscini, Monte Verità, Ascona, Switzerland, July 9-14, 2006. The summit provided a venue for discussions on a broad range of topics. |
| data science in neuroscience: Neuroscience Data in the Cloud National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Health Sciences Policy, Forum on Neuroscience and Nervous System Disorders, 2020-06-06 The cloud model of data sharing has led to a vast increase in the quantity and complexity of data and expanded access to these data, which has attracted many more researchers, enabled multi-national neuroscience collaborations, and facilitated the development of many new tools. Yet, the cloud model has also produced new challenges related to data storage, organization, and protection. Merely switching the technical infrastructure from local repositories to cloud repositories is not enough to optimize data use. To explore the burgeoning use of cloud computing in neuroscience, the National Academies Forum on Neuroscience and Nervous System Disorders hosted a workshop on September 24, 2019. A broad range of stakeholders involved in cloud-based neuroscience initiatives and research explored the use of cloud technology to advance neuroscience research and shared approaches to address current barriers. This publication summarizes the presentation and discussion of the workshop. |
| data science in neuroscience: An Introductory Course in Computational Neuroscience Paul Miller, 2018-10-09 A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits. |
| data science in neuroscience: Challenges and Applications for Implementing Machine Learning in Computer Vision Kashyap, Ramgopal, Kumar, A.V. Senthil, 2019-10-04 Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques. |
| data science in neuroscience: The Spike Mark Humphries, 2021-03-09 The story of a neural impulse and what it reveals about how our brains work We see the last cookie in the box and think, can I take that? We reach a hand out. In the 2.1 seconds that this impulse travels through our brain, billions of neurons communicate with one another, sending blips of voltage through our sensory and motor regions. Neuroscientists call these blips “spikes.” Spikes enable us to do everything: talk, eat, run, see, plan, and decide. In The Spike, Mark Humphries takes readers on the epic journey of a spike through a single, brief reaction. In vivid language, Humphries tells the story of what happens in our brain, what we know about spikes, and what we still have left to understand about them. Drawing on decades of research in neuroscience, Humphries explores how spikes are born, how they are transmitted, and how they lead us to action. He dives into previously unanswered mysteries: Why are most neurons silent? What causes neurons to fire spikes spontaneously, without input from other neurons or the outside world? Why do most spikes fail to reach any destination? Humphries presents a new vision of the brain, one where fundamental computations are carried out by spontaneous spikes that predict what will happen in the world, helping us to perceive, decide, and react quickly enough for our survival. Traversing neuroscience’s expansive terrain, The Spike follows a single electrical response to illuminate how our extraordinary brains work. |
| data science in neuroscience: Statistical Techniques for Neuroscientists Young K. Truong, Mechelle M. Lewis, 2016-10-04 Statistical Techniques for Neuroscientists introduces new and useful methods for data analysis involving simultaneous recording of neuron or large cluster (brain region) neuron activity. The statistical estimation and tests of hypotheses are based on the likelihood principle derived from stationary point processes and time series. Algorithms and software development are given in each chapter to reproduce the computer simulated results described therein. The book examines current statistical methods for solving emerging problems in neuroscience. These methods have been applied to data involving multichannel neural spike train, spike sorting, blind source separation, functional and effective neural connectivity, spatiotemporal modeling, and multimodal neuroimaging techniques. The author provides an overview of various methods being applied to specific research areas of neuroscience, emphasizing statistical principles and their software. The book includes examples and experimental data so that readers can understand the principles and master the methods. The first part of the book deals with the traditional multivariate time series analysis applied to the context of multichannel spike trains and fMRI using respectively the probability structures or likelihood associated with time-to-fire and discrete Fourier transforms (DFT) of point processes. The second part introduces a relatively new form of statistical spatiotemporal modeling for fMRI and EEG data analysis. In addition to neural scientists and statisticians, anyone wishing to employ intense computing methods to extract important features and information directly from data rather than relying heavily on models built on leading cases such as linear regression or Gaussian processes will find this book extremely helpful. |
| data science in neuroscience: Statistical Signal Processing for Neuroscience and Neurotechnology Karim G. Oweiss, 2010-09-22 This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems.Written by experts in the field, the book is an ideal reference for researchers working in the field of neural engineering, neural interface, computational neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the basic principles, theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience. - A comprehensive overview of the specific problems in neuroscience that require application of existing and development of new theory, techniques, and technology by the signal processing community - Contains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience research - Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems |
| data science in neuroscience: From Neuroscience to Neurology Stephen Waxman, 2004-11-05 The field of neurology is being transformed, from a therapeutically nihilistic discipline with few effective treatments, to a therapeutic specialty which offers new, effective treatments for disorders of the brain and spinal cord. This remarkable transformation has bridged neuroscience, molecular medicine, and clinical investigation, and represents a major triumph for biomedical research. This book, which contains chapters by more than 29 internationally recognized authorities who have made major contributions to neurotherapeutics, tells the stories of how new treatments for disabling disorders of the nervous system, such as stroke, multiple sclerosis, Parkinson's disease, and migraine, were developed, and explores evolving themes and technologies that offer hope for even more effective treatments and ultimately cures for currently untreatable disorders of the brain and spinal cord. The first part of this book reviews the development of new therapies in neurology, from their inception in terms of basic science to their introduction into the clinical world. It also explores evolving themes and new technologies. This book will be of interest to everyone – clinicians and basic scientists alike – interested in diseases of the brain and spinal cord, and in the quest for new treatments for these disorders.* Presents the evolution of the field of neurology into a therapeutic discipline * Discusses lessons learned from past successes and applications to ongoing work* Explores the future of this field |
| data science in neuroscience: So You Want to Be a Neuroscientist? Ashley Juavinett, 2020-12-08 The pursuit to understand the human brain in all its intricacy is a fascinatingly complex challenge and neuroscience is one of the fastest-growing scientific fields worldwide. There is a wide range of career options open to those who wish to pursue a career in neuroscience, yet there are few resources that provide students with inside advice on how to go about it. So You Want to Be a Neuroscientist? is a contemporary and engaging guide for aspiring neuroscientists of diverse backgrounds and interests. Fresh with the experience of having recently launched her own career, Ashley Juavinett provides a candid look at the field, offering practical guidance that explores everything from programming to personal stories. Juavinett begins with a look at the field and its history, exploring our evolving understanding of how the brain works. She then tackles the nitty-gritty: how to apply to a PhD program, the daily life of a graduate student, the art of finding mentors and collaborators, and what to expect when working in a lab. Finally, she introduces readers to diverse young scientists whose career paths illustrate what you can do with a neuroscience degree. For anyone intrigued by the brain or seeking advice on how to further their ambitions of studying it, So You Want to Be a Neuroscientist? is a practical and timely overview of how to learn and thrive in this exciting field. |
| data science in neuroscience: Neuronal Dynamics Wulfram Gerstner, Werner M. Kistler, Richard Naud, Liam Paninski, 2014-07-24 This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience. |
| data science in neuroscience: Mathematical and Theoretical Neuroscience Giovanni Naldi, Thierry Nieus, 2018-03-20 This volume gathers contributions from theoretical, experimental and computational researchers who are working on various topics in theoretical/computational/mathematical neuroscience. The focus is on mathematical modeling, analytical and numerical topics, and statistical analysis in neuroscience with applications. The following subjects are considered: mathematical modelling in Neuroscience, analytical and numerical topics; statistical analysis in Neuroscience; Neural Networks; Theoretical Neuroscience. The book is addressed to researchers involved in mathematical models applied to neuroscience. |
| data science in neuroscience: Analysis of Neural Data Robert E. Kass, Uri T. Eden, Emery N. Brown, 2014-07-08 Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work. |
| data science in neuroscience: Stochastic Methods in Neuroscience Carlo Laing, Gabriel J Lord, 2010 Great interest is now being shown in computational and mathematical neuroscience, fuelled in part by the rise in computing power, the ability to record large amounts of neurophysiological data, and advances in stochastic analysis. These techniques are leading to biophysically more realistic models. It has also become clear that both neuroscientists and mathematicians profit from collaborations in this exciting research area.Graduates and researchers in computational neuroscience and stochastic systems, and neuroscientists seeking to learn more about recent advances in the modelling and analysis of noisy neural systems, will benefit from this comprehensive overview. The series of self-contained chapters, each written by experts in their field, covers key topics such as: Markov chain models for ion channel release; stochastically forced single neurons and populations of neurons; statistical methods for parameterestimation; and the numerical approximation of these stochastic models.Each chapter gives an overview of a particular topic, including its history, important results in the area, and future challenges, and the text comes complete with a jargon-busting index of acronyms to allow readers to familiarize themselves with the language used. |
| data science in neuroscience: Decision Neuroscience Jean-Claude Dreher, Léon Tremblay, 2016-09-27 Decision Neuroscience addresses fundamental questions about how the brain makes perceptual, value-based, and more complex decisions in non-social and social contexts. This book presents compelling neuroimaging, electrophysiological, lesional, and neurocomputational models in combination with hormonal and genetic approaches, which have led to a clearer understanding of the neural mechanisms behind how the brain makes decisions. The five parts of the book address distinct but inter-related topics and are designed to serve both as classroom introductions to major subareas in decision neuroscience and as advanced syntheses of all that has been accomplished in the last decade. Part I is devoted to anatomical, neurophysiological, pharmacological, and optogenetics animal studies on reinforcement-guided decision making, such as the representation of instructions, expectations, and outcomes; the updating of action values; and the evaluation process guiding choices between prospective rewards. Part II covers the topic of the neural representations of motivation, perceptual decision making, and value-based decision making in humans, combining neurcomputational models and brain imaging studies. Part III focuses on the rapidly developing field of social decision neuroscience, integrating recent mechanistic understanding of social decisions in both non-human primates and humans. Part IV covers clinical aspects involving disorders of decision making that link together basic research areas including systems, cognitive, and clinical neuroscience; this part examines dysfunctions of decision making in neurological and psychiatric disorders, such as Parkinson's disease, schizophrenia, behavioral addictions, and focal brain lesions. Part V focuses on the roles of various hormones (cortisol, oxytocin, ghrelin/leptine) and genes that underlie inter-individual differences observed with stress, food choices, and social decision-making processes. The volume is essential reading for anyone interested in decision making neuroscience. With contributions that are forward-looking assessments of the current and future issues faced by researchers, Decision Neuroscience is essential reading for anyone interested in decision-making neuroscience. - Provides comprehensive coverage of approaches to studying individual and social decision neuroscience, including primate neurophysiology, brain imaging in healthy humans and in various disorders, and genetic and hormonal influences on decision making - Covers multiple levels of analysis, from molecular mechanisms to neural-systems dynamics and computational models of how we make choices - Discusses clinical implications of process dysfunctions, including schizophrenia, Parkinson's disease, eating disorders, drug addiction, and pathological gambling - Features chapters from top international researchers in the field and full-color presentation throughout with numerous illustrations to highlight key concepts |
| data science in neuroscience: Integrative Neuroscience and Personalized Medicine Evian Gordon, Stephen H. Koslow, 2011 This book takes an in depth and hard look at the current status and future direction of treatment predictive markers in Personalized Medicine for the brain from the perspectives of the researchers on the cutting edge and those involved in healthcare implementation. The contents provide a comprehensive text suitable as both a pithy introduction to and a clear summary of the science to solutions continuum in this developing field of Personalized Medicine and Integrative Neuroscience. The science includes both measures of genes using whole genome approaches and SNIPS as well as BRAINmarkers of direct brain function such as brain imaging, biophysical changes and objective cognitive and behavioral measurements. Personalized Medicine for Brain Disorders will soon be a reality using the comprehensive quantitative and standardized approaches to genomics, BRAINmarkers and cognitive function. Each chapter provides a review of recent relevant literature; show the solutions achieved through integrative neuroscience and applications in patient care thus providing a practical guide to the reader. The timeliness of this book's content is propitious providing bottom line information to educate practicing clinicians, health care workers and researchers, and also a pathway for undergraduate and graduates interested in further their understanding of and involvement in tailored personal solutions. |
| data science in neuroscience: The Design of Experiments in Neuroscience Mary Harrington, 2011 Originally published in 2006, the second edition of The Design of Experiments in Neuroscience continues to be an excellent and eminently readable guideline for students beginning their scientific careers. Although all of the examples are specific to neuroscience, this slender volume offers valuable illumination on core practices, principles, and experimental approaches pertinent for all new researchers. Chapter topics cover recognizing pseudoscience, ethics, how to critically read journal articles, how to pick an experimental question, basic research design, controlling variables, and tips for becoming an independent investigator. Each of the eight chapters provides descriptive figures and extra information boxes, questions to check reader comprehension, additional thought questions, further reading suggestions, and Web resources. The six appendixes are as valuable as the main text, including information on working with data, writing research papers, a sample paper, questions and exercises for review, a glossary, and answers to chapter questions. Neuroscientist Harrington (Smith College) has created a wonderful resource that should be a must read for every neuroscientist in training, if not all novice scientists. Summing Up: Highly recommended. Upper-division undergraduates and graduate students. Upper-division Undergraduates; Graduate Students. Reviewed by C. L. Iwema. |
| data science in neuroscience: Neuroscience Databases Rolf Kötter, 2003 Neuroscience Databases: A Practical Guide is the first book providing a comprehensive overview of these increasingly important databases. This volume makes the results of the Human Genome Project and other recent large-scale initiatives in the neurosciences available to a wider community. It extends the scope of bioinformatics from the molecular to the cellular, microcircuitry and systems levels, dealing for the first time with complex neuroscientific issues and leading the way to a new culture of data sharing and data mining necessary to successfully tackle neuroscience questions. Aimed at the novice user who wants to access the data, it provides clear and concise instructions on how to download the available data sets and how to use the software with a minimum of technical detail with most chapters written by the database creators themselves. Key databases and topics include: -Neuroinformatics for C. Elegans; -Gene Expression Patterns; -Functional Analyses of Olfactory Receptors -Protein-Protein Interactions; -Web-Based Neuronal Archives; -Neuronal and Network Modeling; -Storage and Retrieval of Experimental Data for Biophysically Realistic Modeling; -Analysis of Spike Trains; -Neural Connectivity Patterns; -Software Tools for Neuroimaging; -Data Management, Inspection and Sharing. |
| data science in neuroscience: Neuroscience and Multilingualism Edna Andrews, 2014-09-04 With a sharp focus, this culmination of cutting-edge research offers a new neuroscientific model for analysing multilingualism. Alongside a comprehensive analysis of the theoretical and experimental contributions to the field, it presents new data and analysis obtained from a multilingualism fMRI study. |
| data science in neuroscience: Evolutionary Cognitive Neuroscience Steven Platek, Julian Keenan, Todd Kennedy Shackelford, 2007 An essential reference for the new discipline of evolutionary cognitive neuroscience that defines the field's approach of applying evolutionary theory to guide brain-behavior investigations. Since Darwin we have known that evolution has shaped all organisms and that biological organs—including the brain and the highly crafted animal nervous system—are subject to the pressures of natural and sexual selection. It is only relatively recently, however, that the cognitive neurosciences have begun to apply evolutionary theory and methods to the study of brain and behavior. This landmark reference documents and defines the emerging field of evolutionary cognitive neuroscience. Chapters by leading researchers demonstrate the power of the evolutionary perspective to yield new data, theory, and insights on the evolution and functional modularity of the brain. Evolutionary cognitive neuroscience covers all areas of cognitive neuroscience, from nonhuman brain-behavior relationships to human cognition and consciousness, and each section of Evolutionary Cognitive Neuroscience addresses a different adaptive problem. After an introductory section that outlines the basic tenets of both theory and methodology of an evolutionarily informed cognitive neuroscience, the book treats neuroanatomy from ontogenetic and phylogenetic perspectives and explores reproduction and kin recognition, spatial cognition and language, and self-awareness and social cognition. Notable findings include a theory to explain the extended ontogenetic and brain development periods of big-brained organisms, fMRI research on the neural correlates of romantic attraction, an evolutionary view of sex differences in spatial cognition, a theory of language evolution that draws on recent research on mirror neurons, and evidence for a rudimentary theory of mind in nonhuman primates. A final section discusses the ethical implications of evolutionary cognitive neuroscience and the future of the field. Contributors: C. Davison Ankney, Simon Baron-Cohen, S. Marc Breedlove, William Christiana, Michael Corballis, Robin I. M. Dunbar, Russell Fernald, Helen Fisher, Jonathan Flombaum, Farah Focquaert, Steven J.C. Gaulin, Aaron Goetz, Kevin Guise, Ruben C. Gur, William D. Hopkins, Farzin Irani, Julian Paul Keenan, Michael Kimberly, Stephen Kosslyn, Sarah L. Levin, Lori Marino, David Newlin, Ivan S. Panyavin, Shilpa Patel, Webb Phillips, Steven M. Platek, David Andrew Puts, Katie Rodak, J. Philippe Rushton, Laurie Santos, Todd K. Shackelford, Kyra Singh, Sean T. Stevens, Valerie Stone, Jaime W. Thomson, Gina Volshteyn, Paul Root Wolpe |
| data science in neuroscience: Smarter Tomorrow Elizabeth R. Ricker, 2021-08-17 What if you could upgrade your brain in 15 minutes a day? Let Elizabeth Ricker, an MIT and Harvard-trained brain researcher turned Silicon Valley technologist, show you how. Join Ricker on a wild and edifying romp through the cutting-edge world of neuroscience and biohacking. You'll encounter Olympic athletes, a game show contestant, a memory marvel, a famous CEO, and scientists galore. From Ricker’s decade-long quest, you will learn: ● The brain-based reason so many self-improvement projects fail . . . But how a little-known secret of Nobel Prize winning scientists could finally unlock success ● Which four abilities—both cognitive and emotional—can predict success in work and relationships . . . and a new system for improving all four ● Which seven research-tested tools can supercharge mental performance. They range from low-tech (a surprising new mindset) to downright futuristic (an electrical device for at-home brain stimulation) Best of all, you will learn to upgrade your brain with Ricker’s 20 customizable self-experiments and a sample, 12-week schedule. Ricker distills insights from dozens of interviews and hundreds of research studies from around the world. She tests almost everything on herself, whether it’s nicotine, video games, meditation, or a little-known beverage from the Pacific islands. Some experiments fail hilariously—but others transform her cognition. She is able to sharpen her memory, increase her attention span, boost her mood, and clear her brain fog. By following Ricker’s system, you’ll uncover your own boosts to mental performance, too. Join a growing, global movement of neurohackers revolutionizing their careers and relationships. Let this book change 15 minutes of your day, and it may just change the rest of your life! |
| data science in neuroscience: Do Zombies Dream of Undead Sheep? Timothy Verstynen, Bradley Voytek, 2016-10-04 A look at the true nature of the zombie brain Even if you've never seen a zombie movie or television show, you could identify an undead ghoul if you saw one. With their endless wandering, lumbering gait, insatiable hunger, antisocial behavior, and apparently memory-less existence, zombies are the walking nightmares of our deepest fears. What do these characteristic behaviors reveal about the inner workings of the zombie mind? Could we diagnose zombism as a neurological condition by studying their behavior? In Do Zombies Dream of Undead Sheep?, neuroscientists and zombie enthusiasts Timothy Verstynen and Bradley Voytek apply their neuro-know-how to dissect the puzzle of what has happened to the zombie brain to make the undead act differently than their human prey. Combining tongue-in-cheek analysis with modern neuroscientific principles, Verstynen and Voytek show how zombism can be understood in terms of current knowledge regarding how the brain works. In each chapter, the authors draw on zombie popular culture and identify a characteristic zombie behavior that can be explained using neuroanatomy, neurophysiology, and brain-behavior relationships. Through this exploration they shed light on fundamental neuroscientific questions such as: How does the brain function during sleeping and waking? What neural systems control movement? What is the nature of sensory perception? Walking an ingenious line between seriousness and satire, Do Zombies Dream of Undead Sheep? leverages the popularity of zombie culture in order to give readers a solid foundation in neuroscience. |
| data science in neuroscience: Signal Processing for Neuroscientists Wim van Drongelen, 2006-12-18 Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest background in mathematics, physics, and computer programming. The focus of this text is on what can be considered the 'golden trio' in the signal processing field: averaging, Fourier analysis, and filtering. Techniques such as convolution, correlation, coherence, and wavelet analysis are considered in the context of time and frequency domain analysis. The whole spectrum of signal analysis is covered, ranging from data acquisition to data processing; and from the mathematical background of the analysis to the practical application of processing algorithms. Overall, the approach to the mathematics is informal with a focus on basic understanding of the methods and their interrelationships rather than detailed proofs or derivations. One of the principle goals is to provide the reader with the background required to understand the principles of commercially available analyses software, and to allow him/her to construct his/her own analysis tools in an environment such as MATLAB®. - Multiple color illustrations are integrated in the text - Includes an introduction to biomedical signals, noise characteristics, and recording techniques - Basics and background for more advanced topics can be found in extensive notes and appendices - A Companion Website hosts the MATLAB scripts and several data files: http://www.elsevierdirect.com/companion.jsp?ISBN=9780123708670 |
| data science in neuroscience: Models of the Mind Grace Lindsay, 2021-03-04 The human brain is made up of 85 billion neurons, which are connected by over 100 trillion synapses. For more than a century, a diverse array of researchers searched for a language that could be used to capture the essence of what these neurons do and how they communicate – and how those communications create thoughts, perceptions and actions. The language they were looking for was mathematics, and we would not be able to understand the brain as we do today without it. In Models of the Mind, author and computational neuroscientist Grace Lindsay explains how mathematical models have allowed scientists to understand and describe many of the brain's processes, including decision-making, sensory processing, quantifying memory, and more. She introduces readers to the most important concepts in modern neuroscience, and highlights the tensions that arise when the abstract world of mathematical modelling collides with the messy details of biology. Each chapter of Models of the Mind focuses on mathematical tools that have been applied in a particular area of neuroscience, progressing from the simplest building block of the brain – the individual neuron – through to circuits of interacting neurons, whole brain areas and even the behaviours that brains command. In addition, Grace examines the history of the field, starting with experiments done on frog legs in the late eighteenth century and building to the large models of artificial neural networks that form the basis of modern artificial intelligence. Throughout, she reveals the value of using the elegant language of mathematics to describe the machinery of neuroscience. |
| data science in neuroscience: Mapping the Brain and Its Functions Institute of Medicine, Division of Biobehavioral Sciences and Mental Disorders, Division of Health Sciences Policy, Committee on a National Neural Circuitry Database, 1991-02-01 Significant advances in brain research have been made, but investigators who face the resulting explosion of data need new methods to integrate the pieces of the brain puzzle. Based on the expertise of more than 100 neuroscientists and computer specialists, this new volume examines how computer technology can meet that need. Featuring outstanding color photography, the book presents an overview of the complexity of brain research, which covers the spectrum from human behavior to genetic mechanisms. Advances in vision, substance abuse, pain, and schizophrenia are highlighted. The committee explores the potential benefits of computer graphics, database systems, and communications networks in neuroscience and reviews the available technology. Recommendations center on a proposed Brain Mapping Initiative, with an agenda for implementation and a look at issues such as privacy and accessibility. |
| data science in neuroscience: Signal Processing in Neuroscience Xiaoli Li, 2016-08-31 This book reviews cutting-edge developments in neural signalling processing (NSP), systematically introducing readers to various models and methods in the context of NSP. Neuronal Signal Processing is a comparatively new field in computer sciences and neuroscience, and is rapidly establishing itself as an important tool, one that offers an ideal opportunity to forge stronger links between experimentalists and computer scientists. This new signal-processing tool can be used in conjunction with existing computational tools to analyse neural activity, which is monitored through different sensors such as spike trains, local filed potentials and EEG. The analysis of neural activity can yield vital insights into the function of the brain. This book highlights the contribution of signal processing in the area of computational neuroscience by providing a forum for researchers in this field to share their experiences to date. |
| data science in neuroscience: Neuroscience and Philosophy Felipe De Brigard, Walter Sinnott-Armstrong, 2022-02-01 Philosophers and neuroscientists address central issues in both fields, including morality, action, mental illness, consciousness, perception, and memory. Philosophers and neuroscientists grapple with the same profound questions involving consciousness, perception, behavior, and moral judgment, but only recently have the two disciplines begun to work together. This volume offers fourteen original chapters that address these issues, each written by a team that includes at least one philosopher and one neuroscientist who integrate disciplinary perspectives and reflect the latest research in both fields. Topics include morality, empathy, agency, the self, mental illness, neuroprediction, optogenetics, pain, vision, consciousness, memory, concepts, mind wandering, and the neural basis of psychological categories. The chapters first address basic issues about our social and moral lives: how we decide to act and ought to act toward each other, how we understand each other’s mental states and selves, and how we deal with pressing social problems regarding crime and mental or brain health. The following chapters consider basic issues about our mental lives: how we classify and recall what we experience, how we see and feel objects in the world, how we ponder plans and alternatives, and how our brains make us conscious and create specific mental states. |
| data science in neuroscience: Fundamentals of Computational Neuroscience Thomas Trappenberg, 2010 The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the first edition. Completely redesigned and revised, it introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain. |
| data science in neuroscience: Analyzing the Strategic Role of Neuromarketing and Consumer Neuroscience Atli, Dincer, 2020-06-19 Marketing research in modern business has developed to include more than just data analytics. Today, an emerging interest within scientific marketing researches is the movement away from consumer research toward the use of direct neuroscientific approaches called neuromarketing. For companies to be profitable, they need to utilize the neuromarketing approach to understand how consumers view products and react to marketing, both consciously and unconsciously. Analyzing the Strategic Role of Neuromarketing and Consumer Neuroscience is a key reference source that provides relevant theoretical frameworks and the latest empirical research findings in the neuromarketing field. While highlighting topics such as advertising technologies, consumer behavior, and digital marketing, this publication explores cognitive practices and the methods of engaging customers on a neurological level. This book is ideally designed for marketers, advertisers, product developers, brand managers, consumer behavior analysts, consumer psychologists, managers, executives, behaviorists, business professionals, neuroscientists, academicians, and students. |
| data science in neuroscience: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| data science in neuroscience: The Idea of the Brain Matthew Cobb, 2020-04-21 An elegant, engrossing (Carol Tavris, Wall Street Journal) examination of what we think we know about the brain and why -- despite technological advances -- the workings of our most essential organ remain a mystery. I cannot recommend this book strongly enough.--Henry Marsh, author of Do No Harm For thousands of years, thinkers and scientists have tried to understand what the brain does. Yet, despite the astonishing discoveries of science, we still have only the vaguest idea of how the brain works. In The Idea of the Brain, scientist and historian Matthew Cobb traces how our conception of the brain has evolved over the centuries. Although it might seem to be a story of ever-increasing knowledge of biology, Cobb shows how our ideas about the brain have been shaped by each era's most significant technologies. Today we might think the brain is like a supercomputer. In the past, it has been compared to a telegraph, a telephone exchange, or some kind of hydraulic system. What will we think the brain is like tomorrow, when new technology arises? The result is an essential read for anyone interested in the complex processes that drive science and the forces that have shaped our marvelous brains. |
| data science in neuroscience: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data science in neuroscience: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| data science in neuroscience: Bringing the Neuroscience of Learning to Online Teaching Tracey Tokuhama-Espinosa, 2021 This practical resource draws on the best of neuroscience to inform decision-making about digital learning. We live in unprecedented times that have pushed schools to make many decisions that have been postponed for years. For the first time since the inception of public education, teachers have been invited to redesign the learning landscape by integrating an intelligent selection of digital educational resources and changing pedagogical approaches based on information from the learning sciences. This handbook will help teachers make the most of this opportunity by showing them how to use digital tools to differentiate learning, employ alternative options to standardized testing, personalize learning, prioritize social-emotional skills, and inspire students to think more critically. The author identifies some gems in quality teaching that are amplified in online contexts, including 40 evidence-informed pedagogies from the learning sciences. This book will help all educators move online teaching and learning to new levels of confidence and success. Book Features: Provides quick references to key planning tools like decision-trees, graphics, app recommendations, and step-by-step directions to help teachers create their own online learning courses.Guides teachers through a 12-step model for instructional design that meets both national and international standards.Shows educators how to use an all-new Digital Resource Taxonomy to select resources, and how to research and keep them up to date.Explains why good instructional design and educational technology are complementary with best practices in learning sciences like Mind, Brain, and Education Science.Shares ways teachers can leverage technology to create more time for the personalized aspects of learning. Shows educators how to design online courses with tools that let all students begin at their own starting points and how to differentiate homework.Offers evidence-informed pedagogies to make online intimate and authentic for students. |
| data science in neuroscience: Python in Neuroscience Eilif Muller, James A. Bednar, Markus Diesmann, Marc-Oliver Gewaltig, Michael Hines, Andrew P. Davison, 2015-07-23 Python is rapidly becoming the de facto standard language for systems integration. Python has a large user and developer-base external to theneuroscience community, and a vast module library that facilitates rapid and maintainable development of complex and intricate systems. In this Research Topic, we highlight recent efforts to develop Python modules for the domain of neuroscience software and neuroinformatics: - simulators and simulator interfaces - data collection and analysis - sharing, re-use, storage and databasing of models and data - stimulus generation - parameter search and optimization - visualization - VLSI hardware interfacing. Moreover, we seek to provide a representative overview of existing mature Python modules for neuroscience and neuroinformatics, to demonstrate a critical mass and show that Python is an appropriate choice of interpreter interface for future neuroscience software development. |
Neural data science: accelerating the experiment-analysis …
Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control — developed in lockstep with advances in experimental neurotechnology — …
Data science and its future in large neuroscience …
We describe the tools, technologies, and methods currently in use, and identify several shortcomings of current data science practice. Building on this survey, we develop plans and …
The data science future of neuroscience theory
Markello, Hansen et al. take a pure data science approach to neu-roscience with neuromaps, integrating heterogeneous data types and bringing them together into a unified framework.
A perspective on neuroscience data standardization with …
Oct 9, 2023 · Neurodata Without Borders (NWB), a standard lan-guage for neurophysiology data, has recently emerged as a pow-erful solution for data management, analysis, and sharing. We …
Neuroscience Data Science applications in - neurodata …
The applications of data science will be grouped into several neuroscience sub-fields: 1. Time series analysis of continuous signals derived from physiological, such as EEG …
Cross-disciplinary collaboration in neuroscience: Genetics, …
Deep Science Publishing Chapter 10: Cross-disciplinary collaboration in neuroscience: Genetics, data science, and clinical practice 10.1. Introduction Cross-disciplinary collaboration can …
Data science in neuroscience: Generating insight from rich, …
With the technological advances enabling acquisition of large volumes of patient data, new machine learning models that can combine information from neuroimaging techniques with …
Data science and its future in large neuroscience collaborations
Collaborative neuroscience requires systematic data management and analysis. How this is best done in practice remains unclear. Based on a survey across collaborative neuroscience …
Major in Data Science, Neuroscience Concentration
Data Science is the discovery of knowledge and insight through the analysis of data. As such, it draws on the study of algorithms and their implementation from computer science, the power …
Dynamic and Succinct Statistical Analysis of Neuroscience Data
data that provide unique opportunities to investigate the intricacies of brain function. However, our ability to exploit the dynamic, interactive i. terplay amongst neural processes is limited by the …
A paradigm shift in neuroscience driven by big data - arXiv.org
Research with naturalistic design shows that fMRI data can capture information at the semantic level16, demonstrating the potential of highly controlled neuroimaging data in reflecting …
Big Data in Cognitive Neuroscience: Opportunities and …
In this paper, we review the current practices in cognitive neuroscience, existing datasets and some opportunities and challenges in cognitive neuroscience. The localization of a brain …
Data science opportunities of large language models for …
Large language models (LLMs) are a new asset class in the machine-learning landscape. Here we offer a primer on defining properties of these modeling techniques. We then reflect on new …
NEUROSCIENCE Analysis methods for large-scale neuronal …
We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding …
Deep and shallow data science for multi-scale optical …
Optical imaging of the brain has expanded dramatically in the past two decades. New optics, indicators, and experimental paradigms are now enabling in-vivo imaging from the synaptic to …
Big data from small data: data-sharing in the 'long tail' of …
Long-tail data in neuroscience can be defined as small, granular data sets, collected by individual laboratories in the course of day-to-day research.
A perspective on neuroscience data standardization with …
Neurodata Without Borders (NWB), a standard language for neurophysiology data, has recently emerged as a powerful solution for data management, analysis, and sharing. We here discuss …
Data Science and Behavioral Neuroscience, BS
develop skills in large-scale data manipulation and storage, machine learning, data mining, and information visualization necessary to execute big brain-mapping initiatives including human …
1. Big-data initiatives: A worldwide of neuroscience: A safe
New technologies in neuroscience generate reams of data at an exponentially increasing rate, spurring the design of very-large-scale data-mining initiatives. Several supranational
Integrating data science and neuroscience in developmental ...
This commentary discusses opportunities for advancing the field of developmental psychopathology through the integration of data science and neuroscience approaches. We …
Neural data science: accelerating the experiment-analysis …
Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control — developed in lockstep with advances in experimental neurotechnology — …
Data science and its future in large neuroscience …
We describe the tools, technologies, and methods currently in use, and identify several shortcomings of current data science practice. Building on this survey, we develop plans and …
The data science future of neuroscience theory
Markello, Hansen et al. take a pure data science approach to neu-roscience with neuromaps, integrating heterogeneous data types and bringing them together into a unified framework.
A perspective on neuroscience data standardization with …
Oct 9, 2023 · Neurodata Without Borders (NWB), a standard lan-guage for neurophysiology data, has recently emerged as a pow-erful solution for data management, analysis, and sharing. We …
Neuroscience Data Science applications in - neurodata …
The applications of data science will be grouped into several neuroscience sub-fields: 1. Time series analysis of continuous signals derived from physiological, such as EEG …
Cross-disciplinary collaboration in neuroscience: Genetics, …
Deep Science Publishing Chapter 10: Cross-disciplinary collaboration in neuroscience: Genetics, data science, and clinical practice 10.1. Introduction Cross-disciplinary collaboration can …
Data science in neuroscience: Generating insight from rich, …
With the technological advances enabling acquisition of large volumes of patient data, new machine learning models that can combine information from neuroimaging techniques with …
Data science and its future in large neuroscience …
Collaborative neuroscience requires systematic data management and analysis. How this is best done in practice remains unclear. Based on a survey across collaborative neuroscience …
Major in Data Science, Neuroscience Concentration
Data Science is the discovery of knowledge and insight through the analysis of data. As such, it draws on the study of algorithms and their implementation from computer science, the power …
Dynamic and Succinct Statistical Analysis of Neuroscience Data
data that provide unique opportunities to investigate the intricacies of brain function. However, our ability to exploit the dynamic, interactive i. terplay amongst neural processes is limited by the …
A paradigm shift in neuroscience driven by big data - arXiv.org
Research with naturalistic design shows that fMRI data can capture information at the semantic level16, demonstrating the potential of highly controlled neuroimaging data in reflecting …
Big Data in Cognitive Neuroscience: Opportunities and …
In this paper, we review the current practices in cognitive neuroscience, existing datasets and some opportunities and challenges in cognitive neuroscience. The localization of a brain …
Data science opportunities of large language models for …
Large language models (LLMs) are a new asset class in the machine-learning landscape. Here we offer a primer on defining properties of these modeling techniques. We then reflect on new …
NEUROSCIENCE Analysis methods for large-scale neuronal …
We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding …
Deep and shallow data science for multi-scale optical …
Optical imaging of the brain has expanded dramatically in the past two decades. New optics, indicators, and experimental paradigms are now enabling in-vivo imaging from the synaptic to …
Big data from small data: data-sharing in the 'long tail' of …
Long-tail data in neuroscience can be defined as small, granular data sets, collected by individual laboratories in the course of day-to-day research.
A perspective on neuroscience data standardization with …
Neurodata Without Borders (NWB), a standard language for neurophysiology data, has recently emerged as a powerful solution for data management, analysis, and sharing. We here discuss …
Data Science and Behavioral Neuroscience, BS
develop skills in large-scale data manipulation and storage, machine learning, data mining, and information visualization necessary to execute big brain-mapping initiatives including human …
1. Big-data initiatives: A worldwide of neuroscience: A safe
New technologies in neuroscience generate reams of data at an exponentially increasing rate, spurring the design of very-large-scale data-mining initiatives. Several supranational
Integrating data science and neuroscience in developmental ...
This commentary discusses opportunities for advancing the field of developmental psychopathology through the integration of data science and neuroscience approaches. We …
